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ProbBreed：一种用于计算多环境试验中品种推荐风险的新工具。

ProbBreed: a novel tool for calculating the risk of cultivar recommendation in multienvironment trials.

机构信息

Department of Agronomy, Federal University of Viçosa, Viçosa 36570-900, Brazil.

Department of Agronomy, Iowa State University, Ames, IA 50011, USA.

出版信息

G3 (Bethesda). 2024 Mar 6;14(3). doi: 10.1093/g3journal/jkae013.

DOI:10.1093/g3journal/jkae013
PMID:38243647
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10917492/
Abstract

Neglecting genotype-by-environment interactions in multienvironment trials (MET) increases the risk of flawed cultivar recommendations for growers. Recent advancements in probability theory coupled with cutting-edge software offer a more streamlined decision-making process for selecting suitable candidates across diverse environments. Here, we present the user-friendly ProbBreed package in R, which allows breeders to calculate the probability of a given genotype outperforming competitors under a Bayesian framework. This article outlines the package's basic workflow and highlights its key features, ranging from MET model fitting to estimating the per se and pairwise probabilities of superior performance and stability for selection candidates. Remarkably, only the selection intensity is required to compute these probabilities. By democratizing this complex yet efficient methodology, ProbBreed aims to enhance decision-making and ultimately contribute to more accurate cultivar recommendations in breeding programs.

摘要

在多环境试验 (MET) 中忽略基因型与环境互作会增加为种植者推荐有缺陷品种的风险。概率论的最新进展与尖端软件的结合为在不同环境中选择合适的候选品种提供了更精简的决策过程。在这里，我们在 R 中介绍了用户友好的 ProbBreed 包，它允许育种者在贝叶斯框架下计算给定基因型相对于竞争对手表现更好的概率。本文概述了该软件包的基本工作流程，并强调了其关键特性，从 MET 模型拟合到估计选择候选品种的本身和成对表现和稳定性的概率。值得注意的是，计算这些概率只需要选择强度。通过使这种复杂但高效的方法民主化，ProbBreed 旨在增强决策能力，并最终有助于在育种计划中更准确地推荐品种。

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本文引用的文献

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Theor Appl Genet. 2022 Apr;135(4):1385-1399. doi: 10.1007/s00122-022-04041-y. Epub 2022 Feb 22.
3
Performance of Hamiltonian Monte Carlo and No-U-Turn Sampler for estimating genetic parameters and breeding values.
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Genet Sel Evol. 2019 Dec 10;51(1):73. doi: 10.1186/s12711-019-0515-1.
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